A usage frequency attribute is determined for each biometric feature in a biometric feature database. The usage frequency attribute indicates a matching success frequency of matching the biometric feature to a user having the biometric feature. The biometric features of the user are sorted in descending order of the usage frequency attribute. The sorting is based on a descending order of the usage frequency attributes for a given user. The biometric features in the biometric feature database are stored in descending order. The storing includes providing prioritized access to the biometric feature having a highest value of the usage frequency attribute so that the biometric feature is selected first in response to a request for the biometric feature of the user.
Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A computer-implemented method, comprising: determining a usage frequency attribute for each biometric feature in a biometric feature database using a data processing device that monitors in real time a matching success frequency of matching the biometric feature to a user having the biometric feature, wherein the usage frequency attribute indicates the matching success frequency of matching the biometric feature to the user having the biometric feature; sorting, based on a descending order of the usage frequency attributes for a given user, the biometric features of the user in descending order of the usage frequency attributes; arranging the biometric features in the biometric feature database based on the usage frequency attributes using the data processing device to store the biometric features in the biometric feature database in descending order of the usage frequency attributes, wherein the usage frequency attribute comprises a number of usage times and a latest usage time to calculate a frequency attribute value of the usage frequency attribute; providing prioritized access to the biometric feature in the biometric feature database having a highest frequency attribute value of the usage frequency attribute; and generating a verification code corresponding to the biometric feature of the user in the biometric feature database having the highest frequency attribute value of the usage frequency attribute, so that the verification code and the biometric feature are selected first in response to a request for the biometric feature of the user, wherein the verification code comprises a hash value which is generated based on the verification code and is used to obtain the biometric feature database.
Biometric authentication systems. This invention addresses the need for efficient and prioritized access to biometric features during user verification. The system determines a usage frequency attribute for each biometric feature stored in a database. This attribute is calculated in real-time by monitoring how often a specific biometric feature successfully matches the corresponding user. The usage frequency attribute includes the number of times a feature has been used and the time of its latest usage, which are combined to derive a frequency attribute value. Biometric features for a given user are then sorted in descending order based on these usage frequency attributes. The entire biometric feature database is arranged to store these features in descending order of their usage frequency attributes. This arrangement allows for prioritized access to the biometric feature with the highest frequency attribute value. A verification code, which is a hash value generated based on the verification code itself and used to access the biometric feature database, is then created for this highest-frequency biometric feature. This ensures that the highest-frequency biometric feature and its corresponding verification code are selected first when a request for the user's biometric feature is made, thereby optimizing the verification process.
2. The computer-implemented method of claim 1 , wherein the usage frequency attribute is a mathematical function of at least one of a number of times that the biometric feature has been requested or a most recent usage time that the biometric feature was matched.
This invention relates to biometric authentication systems, specifically improving the efficiency of biometric feature matching by dynamically adjusting the usage frequency of stored biometric features. The problem addressed is the computational and storage overhead in systems that frequently match biometric features, such as fingerprints or facial recognition data, against a large database of stored templates. The solution involves tracking and adjusting the usage frequency of biometric features based on their historical access patterns to optimize system performance. The method calculates a usage frequency attribute for each biometric feature, which is derived from either the number of times the feature has been requested for matching or the most recent time it was successfully matched. This attribute determines how often the feature is prioritized or deprioritized in subsequent matching operations. For example, frequently accessed biometric features may be stored in faster-access memory or prioritized in search algorithms, while rarely used features may be archived or excluded from active matching to reduce computational load. The system dynamically updates the usage frequency attribute as new matching requests are processed, ensuring continuous optimization of resource allocation. This approach reduces latency and improves efficiency in biometric authentication systems by adapting to real-time usage patterns.
3. The computer-implemented method of claim 1 , wherein sorting the biometric features of the user in descending order of the usage frequency attribute comprises: performing a weighted summation on at least two usage frequency attributes for biometric feature to obtain the frequency attribute value when the usage frequency attribute comprises at least two usage frequency attributes; and sorting the biometric features based on the weighted summation.
This invention relates to a computer-implemented method for processing biometric features of a user, particularly focusing on sorting these features based on their usage frequency. The method addresses the challenge of efficiently organizing biometric data when multiple usage frequency attributes are present, ensuring accurate prioritization for applications such as authentication or personalization systems. The method involves sorting biometric features in descending order of their usage frequency. When a biometric feature has multiple usage frequency attributes, the method performs a weighted summation of these attributes to compute a single frequency attribute value. This summation is based on predefined weights assigned to each attribute, allowing for a balanced evaluation. The biometric features are then sorted according to these computed values, enabling systems to prioritize features with higher usage frequency. This approach ensures that biometric features are ranked effectively, even when multiple frequency metrics are involved, improving the reliability and efficiency of biometric-based processes. The weighted summation step prevents inconsistencies that may arise from conflicting or varying frequency attributes, providing a standardized way to prioritize features. The sorted features can then be used in applications requiring biometric data analysis, such as security systems or user profiling.
4. The computer-implemented method of claim 1 , further comprising: comparing a given biometric feature of the user with a given a biometric feature in the biometric feature database; updating a usage frequency attribute of the given biometric feature when a match is determined based on the comparison; and rearranging the biometric features in the biometric feature database based on the updated usage frequency attribute.
A computer-implemented method for managing biometric features in a database involves comparing a user's biometric feature with stored biometric features to identify matches. When a match is found, the system updates a usage frequency attribute associated with the matched biometric feature, tracking how often it is used. The method then rearranges the biometric features in the database based on their updated usage frequency, prioritizing more frequently used features. This approach optimizes biometric authentication by dynamically adjusting the database structure to reflect real-world usage patterns, improving efficiency and accuracy. The system may also include steps for enrolling new biometric features, verifying user identity, and handling multiple biometric modalities. The dynamic rearrangement ensures that the most relevant features are readily accessible, reducing processing time and enhancing security. This method is particularly useful in applications requiring fast and reliable biometric authentication, such as access control systems, financial transactions, or user verification processes.
5. The computer-implemented method of claim 1 , wherein the biometric feature is a facial feature.
A computer-implemented method for biometric authentication focuses on identifying individuals using facial features. The method involves capturing biometric data, such as facial images or patterns, and processing this data to extract unique identifiers. These identifiers are then compared against stored reference data to verify or authenticate the individual. The system may use machine learning or pattern recognition algorithms to enhance accuracy and reliability. The facial feature-based approach improves security by leveraging distinctive facial characteristics, reducing reliance on traditional authentication methods like passwords or PINs. This method is particularly useful in applications requiring high-security access control, such as financial transactions, secure facility entry, or identity verification in digital platforms. The system may also include preprocessing steps to enhance image quality, such as noise reduction or normalization, to ensure consistent and accurate biometric matching. By focusing on facial features, the method provides a non-intrusive and user-friendly authentication solution that minimizes user effort while maintaining robust security.
6. The computer-implemented method of claim 1 , further comprising: obtaining a facial feature of a payer; comparing the obtained facial feature of a payer with the facial features in a facial feature database; and when a matched facial feature is obtained, performing a payment by using a payment account corresponding to the facial feature.
This invention relates to a computer-implemented payment system that uses facial recognition to authenticate and process transactions. The system addresses the need for secure, convenient, and contactless payment methods, particularly in environments where traditional payment methods like cards or mobile devices may be impractical or insecure. The method involves capturing a facial feature of a payer, such as an image or biometric data, and comparing it against a database of pre-registered facial features. Each stored facial feature is linked to a corresponding payment account. When a match is found, the system automatically initiates a payment transaction using the associated account, eliminating the need for physical cards, PINs, or manual authentication steps. This approach enhances security by reducing reliance on easily stolen or lost payment instruments while improving user convenience by enabling seamless, biometric-based transactions. The system may also include additional steps, such as verifying the payer's identity through multiple biometric or behavioral factors to prevent fraud. The payment account linked to the facial feature may be pre-registered by the user, ensuring that only authorized individuals can initiate transactions. This method is particularly useful in retail, hospitality, or digital payment environments where speed and security are critical. The invention aims to streamline payment processes while maintaining robust authentication to protect against unauthorized access.
7. The computer-implemented method of claim 6 , further comprising: obtaining the verification code allocated to the payer, wherein different verification codes are allocated to different payers; and determining, based on the verification code and from a set of facial feature databases, a particular facial feature database to be used for comparing facial features.
This invention relates to secure payment verification using facial recognition, addressing the need for reliable authentication in digital transactions. The method involves verifying a payer's identity by comparing their facial features against a database of stored facial data. To enhance security and accuracy, the system assigns unique verification codes to different payers. When a transaction is initiated, the system retrieves the payer's assigned verification code and uses it to select a specific facial feature database from a set of databases. This ensures that each payer's facial data is compared against the correct database, reducing the risk of false matches or unauthorized access. The method improves authentication accuracy by dynamically linking verification codes to specialized databases, tailored to individual payers. This approach prevents cross-contamination of facial data between users and strengthens security in financial transactions. The system may also include steps for capturing the payer's facial image, extracting facial features, and performing the comparison to confirm identity before authorizing the payment. The use of distinct databases per payer enhances privacy and reduces the likelihood of errors in facial recognition.
8. A non-transitory, computer-readable medium storing one or more instructions executable by a computer system to perform operations comprising: determining a usage frequency attribute for each biometric feature in a biometric feature database using a data processing device that monitors in real time a matching success frequency of matching the biometric feature to a user having the biometric feature, wherein the usage frequency attribute indicates the matching success frequency of matching the biometric feature to the user having the biometric feature; sorting, based on a descending order of the usage frequency attributes for a given user, the biometric features of the user in descending order of the usage frequency attributes; arranging the biometric features in the biometric feature database based on the usage frequency attributes using the data processing device to store the biometric features in the biometric feature database in descending order of the usage frequency attributes, wherein the usage frequency attribute comprises a number of usage times and a latest usage time to calculate a frequency attribute value of the usage frequency attribute; providing prioritized access to the biometric feature in the biometric feature database having a highest frequency attribute value of the usage frequency attribute; and generating a verification code corresponding to the biometric feature of the user in the biometric feature database having the highest frequency attribute value of the usage frequency attribute, so that the verification code and the biometric feature are selected first in response to a request for the biometric feature of the user, wherein the verification code comprises a hash value which is generated based on the verification code and is used to obtain the biometric feature database.
This invention relates to biometric authentication systems, specifically optimizing biometric feature selection for efficient and secure user verification. The system addresses the challenge of managing multiple biometric features for a user, ensuring the most reliable and frequently used features are prioritized during authentication. A data processing device monitors in real time the matching success frequency of each biometric feature in a database, calculating a usage frequency attribute that includes the number of usage times and the latest usage time to determine a frequency attribute value. The biometric features are then sorted in descending order of this value, with the highest-frequency features stored and accessed first. When a verification request is made, the system prioritizes the biometric feature with the highest frequency attribute value, generating a corresponding verification code—a hash value derived from the feature—to streamline access. This approach enhances authentication efficiency by reducing search time and improving success rates, while maintaining security through hashed verification codes. The system dynamically adapts to user behavior, ensuring optimal performance over time.
9. The non-transitory, computer-readable medium of claim 8 , wherein the usage frequency attribute is a mathematical function of at least one of a number of times that the biometric feature has been requested or a most recent usage time that the biometric feature was matched.
A system and method for managing biometric feature data in a computing environment involves storing biometric features in a database and controlling access to these features based on usage frequency. The system tracks how often each biometric feature is requested and the most recent time it was matched, using these metrics to determine whether to retain or delete the feature. The usage frequency attribute is calculated as a mathematical function of either the number of times the biometric feature has been requested or the most recent usage time it was matched. This approach ensures that frequently used or recently accessed biometric features are retained, while less frequently used or outdated features are removed to optimize storage and improve system efficiency. The system may also include a biometric feature database, a usage frequency tracker, and a deletion module that removes features based on the calculated usage frequency. This method helps maintain an up-to-date and relevant set of biometric data, reducing storage requirements and enhancing security by eliminating obsolete or rarely used biometric records.
10. The non-transitory, computer-readable medium of claim 8 , wherein sorting the biometric features of the user in descending order of the usage frequency attribute comprises: performing a weighted summation on at least two usage frequency attributes for biometric feature to obtain the frequency attribute value when the usage frequency attribute comprises at least two usage frequency attributes; and sorting the biometric features based on the weighted summation.
This invention relates to biometric authentication systems, specifically improving the efficiency and accuracy of user verification by optimizing the selection and ordering of biometric features. The problem addressed is the computational inefficiency and potential inaccuracies in biometric authentication when processing multiple biometric features without prioritization. The solution involves dynamically sorting biometric features based on their usage frequency to enhance authentication performance. The system stores biometric features of a user, each associated with a usage frequency attribute indicating how often the feature is used in authentication. When multiple usage frequency attributes exist for a biometric feature, the system performs a weighted summation of these attributes to derive a single frequency attribute value. This value is then used to sort the biometric features in descending order of their usage frequency. The sorted features are prioritized during authentication, ensuring the most frequently used and reliable features are processed first, reducing computational overhead and improving accuracy. The weighted summation allows for flexible integration of different frequency metrics, such as historical usage counts or confidence levels, into a unified ranking system. This approach optimizes the authentication process by focusing on the most relevant biometric features, enhancing both speed and reliability.
11. The non-transitory, computer-readable medium of claim 8 , further comprising: comparing a given biometric feature of the user with a given a biometric feature in the biometric feature database; updating a usage frequency attribute of the given biometric feature when a match is determined based on the comparison; and rearranging the biometric features in the biometric feature database based on the updated usage frequency attribute.
A system for managing biometric authentication features dynamically updates and prioritizes biometric data based on usage frequency. The technology addresses the challenge of efficiently organizing biometric features in authentication systems to improve accuracy and response times. The system includes a biometric feature database storing multiple biometric features associated with a user, each with a usage frequency attribute indicating how often the feature is used for authentication. When a user attempts authentication, the system compares the provided biometric feature (e.g., fingerprint, facial recognition data) with stored features in the database. If a match is found, the system updates the usage frequency attribute of the matched feature, reflecting its increased relevance. The database then rearranges the biometric features based on their updated usage frequencies, prioritizing more frequently used features for faster access and higher accuracy in future authentications. This dynamic adjustment ensures the system adapts to the user's behavior, optimizing performance over time. The approach enhances security by focusing on the most reliable and commonly used biometric features while maintaining flexibility for less frequently used alternatives.
12. The non-transitory, computer-readable medium of claim 8 , wherein the biometric feature is a facial feature.
A system and method for biometric authentication processes facial recognition to verify user identity. The invention addresses the need for secure and efficient identity verification in digital systems, particularly where traditional authentication methods like passwords are vulnerable to breaches. The system captures and analyzes facial features to generate a biometric template, which is then compared against stored reference data to authenticate the user. The facial recognition process involves extracting key facial characteristics, such as the relative positions of eyes, nose, and mouth, and using these features to create a unique biometric signature. This signature is matched against pre-enrolled user data to confirm identity. The system may also incorporate additional security measures, such as liveness detection, to prevent spoofing attacks using static images or masks. By relying on facial features, the invention provides a non-intrusive and user-friendly authentication method that enhances security while reducing reliance on memorized credentials. The technology is applicable in various domains, including mobile devices, access control systems, and financial transactions, where secure and convenient authentication is critical. The use of facial recognition improves accuracy and reduces false acceptance rates, making it a robust solution for identity verification.
13. The non-transitory, computer-readable medium of claim 8 , further comprising: obtaining a facial feature of a payer; comparing the obtained facial feature of a payer with the facial features in a facial feature database; and when a matched facial feature is obtained, performing a payment by using a payment account corresponding to the facial feature.
This invention relates to a facial recognition-based payment system that automates transactions by identifying a payer using facial features. The system addresses the need for secure, convenient, and contactless payment methods, particularly in environments where traditional payment methods like cards or mobile devices are impractical or inefficient. The system includes a facial feature database storing pre-registered facial data linked to payment accounts. When a payer initiates a transaction, the system captures their facial image, extracts key facial features, and compares them against the database. If a match is found, the corresponding payment account is automatically used to complete the transaction without requiring additional authentication steps. The system may also integrate with other security measures, such as liveness detection, to prevent fraud. The invention improves upon existing payment methods by eliminating the need for physical cards or manual input, reducing transaction time, and enhancing security through biometric verification. It is particularly useful in retail, hospitality, and public transportation settings where speed and convenience are critical. The system ensures that only authorized users can access linked payment accounts, mitigating risks associated with lost or stolen payment devices.
14. The non-transitory, computer-readable medium of claim 13 , further comprising: obtaining the verification code allocated to the payer, wherein different verification codes are allocated to different payers; and determining, based on the verification code and from a set of facial feature databases, a particular facial feature database to be used for comparing facial features.
This invention relates to secure payment verification using facial recognition, addressing the need for reliable authentication in digital transactions. The system involves a computer-readable medium storing instructions for verifying a payer's identity by comparing their facial features against a database. The method includes capturing an image of the payer's face, extracting facial features from the image, and comparing these features to stored facial data. To enhance security, the system assigns unique verification codes to different payers. These codes are used to select a specific facial feature database from a set of databases for the comparison process. This ensures that each payer's data is isolated, reducing the risk of unauthorized access or cross-verification errors. The system dynamically determines the appropriate database based on the verification code, improving accuracy and security in facial recognition-based authentication. The approach helps prevent fraud by ensuring that only authorized users can complete transactions, leveraging unique identifiers to maintain data integrity.
15. A computer-implemented system, comprising: one or more computers; and one or more computer memory devices interoperably coupled with the one or more computers and having tangible, non-transitory, machine-readable media storing one or more instructions that, when executed by the one or more computers, perform one or more operations comprising: determining a usage frequency attribute for each biometric feature in a biometric feature database using a data processing device that monitors in real time a matching success frequency of matching the biometric feature to a user having the biometric feature, wherein the usage frequency attribute indicates the matching success frequency of matching the biometric feature to the user having the biometric feature; sorting, based on a descending order of the usage frequency attributes for a given user, the biometric features of the user in descending order of the usage frequency attributes; arranging the biometric features in the biometric feature database based on the usage frequency attributes using the data processing device to store the biometric features in the biometric feature database in descending order of the usage frequency attributes, wherein the usage frequency attribute comprises a number of usage times and a latest usage time to calculate a frequency attribute value of the usage frequency attribute; providing prioritized access to the biometric feature in the biometric feature database having a highest frequency attribute value of the usage frequency attribute; and generating a verification code corresponding to the biometric feature of the user in the biometric feature database having the highest frequency attribute value of the usage frequency attribute, so that the verification code and the biometric feature are selected first in response to a request for the biometric feature of the user, wherein the verification code comprises a hash value which is generated based on the verification code and is used to obtain the biometric feature database.
A computer-implemented system monitors and optimizes biometric feature usage in authentication processes. The system addresses inefficiencies in biometric authentication by dynamically prioritizing frequently used and recently accessed biometric features, reducing latency and improving success rates. The system includes one or more computers and memory devices storing instructions for tracking the matching success frequency of biometric features in real time. Each biometric feature is assigned a usage frequency attribute, which combines the number of usage times and the latest usage time to calculate a frequency attribute value. The system sorts biometric features in descending order of these values, storing them in a database accordingly. When a request for a user's biometric feature is received, the system prioritizes access to the feature with the highest frequency attribute value, generating a corresponding verification code. This verification code, a hash value derived from the biometric feature, ensures secure and efficient retrieval. The system enhances authentication speed and reliability by dynamically adjusting feature prioritization based on real-time usage data.
16. The computer-implemented system of claim 15 , wherein the usage frequency attribute is a mathematical function of at least one of a number of times that the biometric feature has been requested or a most recent usage time that the biometric feature was matched.
A computer-implemented system for managing biometric feature usage in authentication processes addresses the challenge of efficiently tracking and prioritizing biometric data to enhance security and performance. The system monitors the frequency and recency of biometric feature requests, such as fingerprint or facial recognition data, to determine their relevance and reliability. By analyzing the number of times a biometric feature has been requested or the most recent time it was successfully matched, the system calculates a usage frequency attribute. This attribute helps prioritize biometric features based on their historical usage patterns, ensuring that frequently or recently used features are given higher priority in authentication workflows. The system dynamically adjusts access control decisions by leveraging this usage data, improving response times and reducing the risk of unauthorized access. This approach optimizes biometric authentication by balancing performance and security, particularly in environments where multiple users or devices rely on biometric verification. The system integrates with existing authentication frameworks to provide real-time adjustments based on biometric feature usage trends, enhancing overall system efficiency and reliability.
17. The computer-implemented system of claim 15 , wherein sorting the biometric features of the user in descending order of the usage frequency attributes comprises: performing a weighted summation on at least two usage frequency attributes for biometric feature to obtain the frequency attribute value when the usage frequency attribute comprises at least two usage frequency attributes; and sorting the biometric features based on the weighted summation.
This invention relates to a computer-implemented system for managing biometric features, specifically focusing on sorting these features based on their usage frequency. The system addresses the challenge of efficiently organizing biometric data to prioritize features that are most frequently used, which is critical for applications like authentication, security, or user identification. The system processes biometric features by first extracting usage frequency attributes associated with each feature. When multiple usage frequency attributes exist for a single biometric feature, the system performs a weighted summation of these attributes to generate a consolidated frequency attribute value. This weighted summation ensures that all relevant usage data is considered in a balanced manner, preventing any single attribute from disproportionately influencing the sorting process. After computing the frequency attribute values, the system sorts the biometric features in descending order based on these values. This sorted arrangement allows the system to prioritize features that are most frequently used, which can improve the accuracy and efficiency of biometric-based processes. The weighted summation step ensures that the sorting is robust and adaptable to different scenarios where multiple usage metrics may be available. This approach is particularly useful in dynamic environments where biometric feature usage patterns may change over time.
18. The computer-implemented system of claim 15 , further comprising: comparing a given biometric feature of the user with a given a biometric feature in the biometric feature database; updating a usage frequency attribute of the given biometric feature when a match is determined based on the comparison; and rearranging the biometric features in the biometric feature database based on the updated usage frequency attribute.
A computer-implemented system enhances biometric authentication by dynamically managing biometric feature databases. The system addresses inefficiencies in static biometric databases, where frequently used features may not be prioritized, leading to slower authentication or higher computational overhead. The system includes a biometric feature database storing multiple biometric features associated with users, each feature having a usage frequency attribute. During authentication, the system compares a user's provided biometric feature (e.g., fingerprint, facial recognition data) with stored features in the database. When a match is found, the system updates the usage frequency attribute of the matched feature, indicating its increased relevance. The database then rearranges its features based on updated usage frequencies, prioritizing frequently accessed features for faster retrieval. This dynamic adjustment optimizes authentication speed and resource utilization by ensuring high-frequency biometric features are readily accessible. The system may also include additional components, such as a feature extraction module to process raw biometric data into comparable features and an authentication module to verify matches against stored features. The overall approach improves biometric system performance by adapting to usage patterns.
19. The computer-implemented system of claim 15 , wherein the biometric feature is a facial feature.
A computer-implemented system for biometric authentication processes facial features to verify user identity. The system captures and analyzes facial data, such as facial geometry, landmarks, or texture patterns, to generate a biometric template. This template is compared against stored reference data to authenticate the user. The system may include a camera or sensor to capture facial images, a processing unit to extract and analyze biometric features, and a database to store and retrieve reference templates. The system may also incorporate liveness detection to prevent spoofing attempts, ensuring that the captured facial data originates from a live person. Additionally, the system may support multi-factor authentication by combining facial recognition with other biometric or non-biometric factors. The system is designed for applications in security, access control, and identity verification, providing a secure and convenient method for user authentication. The facial recognition process may involve machine learning algorithms to improve accuracy and adapt to variations in lighting, pose, or facial expressions. The system may also include privacy-preserving measures, such as on-device processing or encryption, to protect user data.
20. The computer-implemented system of claim 15 , further comprising: obtaining a facial feature of a payer; comparing the obtained facial feature of a payer with the facial features in a facial feature database; and when a matched facial feature is obtained, performing a payment by using a payment account corresponding to the facial feature.
This invention relates to a computer-implemented payment system that uses facial recognition to authenticate and authorize transactions. The system addresses the problem of secure and convenient payment methods by eliminating the need for physical cards, PINs, or manual input, reducing fraud and improving user experience. The system includes a facial recognition module that captures and processes facial features of a payer. These features are compared against a database of pre-registered facial templates linked to payment accounts. When a match is found, the system automatically initiates a payment using the corresponding account. The database stores facial feature data and associated payment account information, ensuring secure and quick access to authorized users. The system may also include a transaction processing module that handles payment execution, ensuring compliance with financial regulations and security protocols. Additionally, it may incorporate fraud detection mechanisms to verify transaction legitimacy before processing. The facial recognition process is designed to be fast and accurate, minimizing delays while maintaining security. This approach enhances payment convenience by allowing users to complete transactions with minimal interaction, while reducing reliance on traditional authentication methods. The system is particularly useful in retail, online payments, and automated kiosks where speed and security are critical.
Cooperative Patent Classification codes for this invention. Click any code to explore related patents in that topic.
September 28, 2020
March 8, 2022
Browse 5M+ US patents with plain-English claim translations and AI-generated analysis.